def _func(self, m, me, la, n, f, g, xnew):
""" Return ndarrays containing the function and constraint
evaluations.
Note: m, me, la, n, f, and g are unused inputs."""
self.set_parameters(xnew)
super(SLSQPdriver, self).run_iteration()
f = self.eval_objective()
if isnan(f):
msg = "Numerical overflow in the objective."
self.raise_exception(msg, RuntimeError)
# Constraints
if self.ncon > 0 :
con_list = []
for v in self.get_constraints().values():
val = v.evaluate(self.parent)
if '>' in val[2]:
con_list.append(val[0]-val[1])
else:
con_list.append(val[1]-val[0])
g = array(con_list)
if self.iprint > 0:
pyflush(self.iout)
# Write out some relevant information to the recorder
self.record_case()
return f, g
def _func(self, m, me, la, n, f, g, xnew):
""" Return ndarrays containing the function and constraint
evaluations.
Note: m, me, la, n, f, and g are unused inputs."""
self.set_parameters(xnew)
super(SLSQPdriver, self).run_iteration()
f = self.eval_objective()
if isnan(f):
msg = "Numerical overflow in the objective."
self.raise_exception(msg, RuntimeError)
# Constraints. Note that SLSQP defines positive as satisfied.
if self.ncon > 0 :
con_list = [-v.evaluate(self.parent) for \
v in self.get_constraints().values()]
g = array(con_list)
if self.iprint > 0:
pyflush(self.iout)
# Write out some relevant information to the recorder
self.record_case()
return f, g
def _func(self, m, me, la, n, f, g, xnew):
""" Return ndarrays containing the function and constraint
evaluations.
Note: m, me, la, n, f, and g are unused inputs."""
self.set_parameters(xnew)
super(SLSQPdriver, self).run_iteration()
f = self.eval_objective()
if isnan(f):
msg = "Numerical overflow in the objective"
self.raise_exception(msg, RuntimeError)
# Constraints
if self.ncon > 0 :
con_list = []
for v in self.get_constraints().values():
val = v.evaluate(self.parent)
if '>' in val[2]:
con_list.append(val[0]-val[1])
else:
con_list.append(val[1]-val[0])
g = array(con_list)
if self.iprint > 0:
pyflush(self.iout)
# Write out some relevant information to the recorder
if self.recorders:
case_input = []
for var, val in zip(self.get_parameters().keys(), xnew):
case_name = var[0] if isinstance(var, tuple) else var
case_input.append([case_name, val])
if self.printvars:
case_output = [(name,
ExprEvaluator(name, scope=self.parent).evaluate())
for name in self.printvars]
else:
case_output = []
case_output.append(["objective", f])
for i, val in enumerate(g):
case_output.append(["Constraint%d" % i, val])
case = Case(case_input, case_output, parent_uuid=self._case_id)
for recorder in self.recorders:
recorder.record(case)
return f, g
def _func(self, m, me, la, n, f, g, xnew):
""" Return ndarrays containing the function and constraint
evaluations.
Note: m, me, la, n, f, and g are unused inputs."""
self.set_parameters(xnew)
super(SLSQPdriver, self).run_iteration()
f = self.eval_objective()
if isnan(f):
msg = "Numerical overflow in the objective."
self.raise_exception(msg, RuntimeError)
# Constraints. Note that SLSQP defines positive as satisfied.
if self.ncon > 0:
g = -1. * array(self.eval_constraints(self.parent))
if self.iprint > 0:
pyflush(self.iout)
return f, g
def _func(self, m, me, la, n, f, g, xnew):
""" Return ndarrays containing the function and constraint
evaluations.
Note: m, me, la, n, f, and g are unused inputs."""
self.set_parameters(xnew)
super(SLSQPdriver, self).run_iteration()
f = self.eval_objective()
if isnan(f):
msg = "Numerical overflow in the objective."
self.raise_exception(msg, RuntimeError)
# Constraints. Note that SLSQP defines positive as satisfied.
if self.ncon > 0:
g = -1. * array(self.eval_constraints(self.parent))
if self.iprint > 0:
pyflush(self.iout)
return f, g
